Triple

T11992235
Position Surface form Disambiguated ID Type / Status
Subject Theo Faron E285433 entity
Predicate allies P2865 FINISHED
Object Marichka
Marichka is a key supporting character in the dystopian film "Children of Men," known for helping protect the first pregnant woman in years.
 E958551 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Marichka | Statement: [Theo Faron, allies, Marichka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marichka
Context triple: [Theo Faron, allies, Marichka]
  • A. Sashenka
    Sashenka is a Russian diminutive form of the given name Aleksandr (Alexander), often used as an affectionate nickname.
  • B. Grushenka
    Grushenka is a central female character in Fyodor Dostoevsky's novel "The Brothers Karamazov," known for her complex mix of sensuality, capriciousness, and capacity for moral and spiritual transformation.
  • C. Masha
    Masha is a town in southwestern Ethiopia that serves as an administrative and commercial center in the Sheka Zone.
  • D. Masha
    Masha is a diminutive and affectionate Russian form of the given name Mary (Maria).
  • E. Mila
    Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Marichka
Triple: [Theo Faron, allies, Marichka]
Generated description
Marichka is a key supporting character in the dystopian film "Children of Men," known for helping protect the first pregnant woman in years.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marichka
Target entity description: Marichka is a key supporting character in the dystopian film "Children of Men," known for helping protect the first pregnant woman in years.
  • A. Sashenka
    Sashenka is a Russian diminutive form of the given name Aleksandr (Alexander), often used as an affectionate nickname.
  • B. Grushenka
    Grushenka is a central female character in Fyodor Dostoevsky's novel "The Brothers Karamazov," known for her complex mix of sensuality, capriciousness, and capacity for moral and spiritual transformation.
  • C. Masha
    Masha is a diminutive and affectionate Russian form of the given name Mary (Maria).
  • D. Masha
    Masha is a town in southwestern Ethiopia that serves as an administrative and commercial center in the Sheka Zone.
  • E. Mila
    Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d6ab44a77c8190a652f4b27164e4ef completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903b11ac481909866b611380792e7 completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f4726696dc8190bf2a7aa43cb08b19 completed May 1, 2026, 9:29 a.m.
NEDg Description generation batch_69f47b7d4ef081908f7f87d90c00d9ed completed May 1, 2026, 10:07 a.m.
NED2 Entity disambiguation (via description) batch_69f47de8c9e48190af01918c9cd94c7d completed May 1, 2026, 10:18 a.m.
Created at: April 8, 2026, 9:46 p.m.